Identifying fault categories, especially for compound faults, is a challenging task in
mechanical fault diagnosis. For this task, this paper proposes a novel intelligent method …

Measures of relevance between features play an important role in classification and
regression analysis. Mutual information has been proved an effective measure for decision …

Feature selection is a significant preprocessing technique for data mining, which can
promote the accuracy of data classification and shrink feature space by eliminating …

With the development of bioinformatics, tumor classification from gene expression data
becomes an important useful technology for cancer diagnosis. Since a gene expression …

Online streaming feature selection, as a new approach which deals with feature streams in
an online manner, has attracted much attention in recent years and played a critical role in …

Rough set theory has become an effective tool to address uncertain decision-making
problems. Nevertheless, existing rough set-based decision-making methods cannot …

Neighborhood-based attribute reduction plays a vital role in pattern recognition, for selecting
a series of informative and relevant attributes from data sets. The increase in dimensionality …

This paper presents a feature selection method for Internet of Things (IoT) information
processing, called MIMIC_FS. The maximal information coefficient (MIC), which can capture …

Neighborhood Learning (NL) is a paradigm covering theories and techniques of
neighborhood, which facilitates data organization, representation and generalization. While …

Kernel machines and rough sets are two classes of commonly exploited learning
techniques. Kernel machines enhance traditional learning algorithms by bringing …